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Sparse kernel density estimation technique based on zero-norm constraint

Hong, X., Chen, S. and Harris, C. J. (2010) Sparse kernel density estimation technique based on zero-norm constraint. In: The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 3782-3787. ISBN 9781424469161

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To link to this item DOI: 10.1109/IJCNN.2010.5596853


A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the -optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:16724
Additional Information:The conference was held in Barcelona, Spain, 18-23 July 2010.


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